Quality of experience aware network selection model for service provisioning in heterogeneous network

Heterogeneous wireless networks (HWNs) are capable of integrating the different radio access technologies that make it possible to connect mobile users based on the performance parameters. Further quality of service (QoS) is one of the major topics for HWNs, moreover existing radio access technology (RAT) methodology are designed to provide network QoS criteria. However, limited work has been carried out for the RAT selection mechanism considering user QoS preference and existing models are developed based on the multi-mode terminal under a given minimal density network. For overcoming research issues this paper present quality of experience (QoE) RAT (QOE-RAT) selection methodology, incorporating both network performance criteria and user preference considering multiple call and multi-mode HWNs environment. First, this paper presents fuzzy preference aware weight (FPAW) and multi-mode terminal preference aware TOPSIS (MMTPA-TOPSIS) for choosing the best RAT for gaining multi-services. Experiment outcomes show the QOE-RAT selection method achieves much superior packet transmission outcomes when compared with state-of-art Rat selection methodologies.

A Survey on Haptic Technologies for Mobile Augmented Reality

Augmented reality (AR) applications have gained much research and industry attention. Moreover, the mobile counterpart—mobile augmented reality (MAR) is one of the most explosive growth areas for AR applications in the mobile environment (e.g., smartphones). The technical improvements in the hardware of smartphones, tablets, and smart-glasses provide an advantage for the wide use of mobile AR in the real world and experience these AR applications anywhere. However, the mobile nature of MAR applications can limit users’ interaction capabilities, such as input and haptic feedback. In this survey, we analyze current research issues in the area of human-computer interaction for haptic technologies in MAR scenarios. The survey first presents human sensing capabilities and their applicability in AR applications. We classify haptic devices into two groups according to the triggered sense: cutaneous/tactile : touch, active surfaces, and mid-air; kinesthetic : manipulandum, grasp, and exoskeleton. Due to MAR applications’ mobile capabilities, we mainly focus our study on wearable haptic devices for each category and their AR possibilities. To conclude, we discuss the future paths that haptic feedback should follow for MAR applications and their challenges.

Segmentation Trachea and Bronchial Branches in Chest Computed Tomography Image by Deep Learning - preliminary results -

Segmentation is one of the most common methods for analyzing and processing medical images, assisting doctors in making accurate diagnoses by providing detailed information about the required body part. However, segmenting medical images presents a number of challenges, including the need for medical professionals to be trained, the fact that it is time-consuming and prone to errors. As a result, it appears that an automated medical image segmentation system is required. Deep learning algorithms have recently demonstrated superior performance for segmentation tasks, particularly semantic segmentation networks that provide a pixel-level understanding of images. U- Net for image segmentation is one of the modern complex networks in the field of medical imaging; several segmentation networks have been built on its foundation with the advancements of Recurrent Residual convolutional units and the construction of recurrent residual convolutional neural network based on U-Net (R2U-Net). R2U-Net is used to perform trachea and bronchial segmentation on a dataset of 36,000 images. With a variety of experiments, the proposed segmentation resulted in a dice-coefficient of 0.8394 on the test dataset. Finally, a number of research issues are raised, indicating the need for future improvements.

Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data

With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.

The co-evolution of remote work and expectations in a COVID-19 world utilizing an expectation disconfirmation theory lens

Purpose As researchers are being called to examine the evolving technology research issues for COVID-19 and other pandemics, remote work has been accelerated and represents the future of work. Although it is known that one of the top forces shaping the future of work is changing employee expectations, the knowledge of remote work during a pandemic remains scant. Thus, this paper aims to determine the impact of remote worker’s expectations on their level of satisfaction and intention to continue to work remotely. Design/methodology/approach Using one of the prominent theories on expectations, Expectation Disconfirmation Theory (EDT), the authors conduct an online survey of 146 individuals who are currently working remotely. Findings By applying EDT, the findings demonstrate that an individual’s expectations regarding remote work impact their level of satisfaction with remote work and intention to continue to work remotely. Incorporating extant research, the findings extend the research stream to indicate that employees’ expectations about remote work significantly impact both their level of satisfaction and level of productivity. Originality/value The discussion elucidates the significance of understanding employee expectations regarding remote work in the evolving new normal. The findings from the study demonstrate the importance of an individual’s expectations regarding remote work on their level of satisfaction with remote work and intention to continue to work remotely. Thus, this study fills a gap in the literature by applying EDT to the remote work context.

Blockchain Interoperability: Towards a Sustainable Payment System

The highly fragmented blockchain and cryptocurrency ecosystem necessitates interoperability mechanisms as a requirement for blockchain-technology acceptance. The immediate implication of interchain interoperability is automatic swapping between cryptocurrencies. We performed a systematic review of the existing literature on Blockchain interoperability and atomic cross-chain transactions. We investigated different blockchain interoperability approaches, including industrial solutions, categorized them and identified the key mechanisms used, and list several example projects for each category. We focused on the atomic transactions between blockchain, a process also known as atomic swap. Furthermore, we studied recent implementations along with architectural approaches for atomic swap and deduced research issues and challenges in cross-chain interoperability and atomic swap. Atomic swap can instantly transfer tokens and significantly reduce the associated costs without using any centralized authority, and thus facilitates the development of a sustainable payment system for wider financial inclusion.

Virtual Reality and Social Work

Virtual reality in social work education and practice is relatively new. There is not a large literature on it—note that several of the resources below are authored by the same colleagues. Given the rapid evolution of the technologies, there are limited resources in terms of works within the last fifteen years. Juried resources published by recognized experts are provided. There are basically two distinct forms. First, we have virtual worlds such as Second Life where controlled avatars explore simulated environments. Virtual worlds can be quite varied and rich in visual content. Complete creation of hospitals, service agencies, schools, and places of worship are possible. Support groups for a variety of problems and ability challenges can regularly meet “in world.” Participation is usually synchronous. Most virtual worlds are accessible via personal computers. Participation costs are generally absent. Virtual worlds are not “games” but instead are platforms in which games may be played, role plays may be staged, classes and seminars held. The second virtual reality technology is generally found in laboratory settings. Participants don 3D helmets or goggles and explore environments that are computer-based. Purposes for creating and establishing these environments vary. For example, people suffering from PTSD can explore and relive traumatic events with therapeutic guidance towards symptom relief. As in the case of virtual worlds, lab-based simulations are usually synchronous. Just as avatars may interact with each other, lab-based experiences can include multiple participants. Each of these technologies offers promise for social work education and practice. Students in distance education can work together even when separated by oceans. Students can engage in service evaluation in virtual worlds. Students can learn about addiction triggers through creating the 3D environments that have modeled them. Both formats may be termed multi-user virtual environments (MUVEs) though terms vary. Of interest, if one looks at this bibliography as a data sample, educational uses tend to be through virtual worlds while practice uses may tend to be more in laboratory settings. The opening section discusses critical professional issues that may apply to using virtual reality innovations in social work. The next sections take up educational and practice applications. Articles that predominantly address research issues follow. Finally, resources for developing virtual world experiences are provided.

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population’s health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study’s primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

ВИКОРИСТАННЯ ХАКАТОН-ЕКОСИСТЕМИ ДЛЯ ФОРСАЙТУ ЗАЙНЯТОСТІ МОЛОДІ: МАСШТАБИ, СТРУКТУРА ТА ФУНКЦІЇ

This article is an attempt to explore the opportunities of using the Hackathon ecosystem tools to perform a quality forecasting of a wide range of youth employment issues. It is observed that despite vast experience of psychological training in higher education institutions based on the transformation of self-awareness, shaping professional perceptions related to changes in professional knowledge of professional occupation as it is, its object, professional community, etc., modern University realia lack targeted management efforts towards developing job-related skills as well as comprehensive assessment of their changing trends in the process of professional enhancement of students’ self-consciousness. At best, specific good practice of individual specialists is used, often without its interpretation by teachers and psychologists and beyond the scope of systemic analysis of the research issues. All this challenges an impetus to further studies on developing professional self-awareness of future specialists and encourages active implementation of the Hackathon ecosystem tools to render a foresight on youth secondary employment and self-employment at the level of University as well as in a regional setting. To attain the research agenda, the study employed the following general and special research methods: a system analysis method, an analytical grouping technique, comparative analysis, and dynamic and graph series construction. To summarise the research outcomes and to prepare a proposal draft on the opportunities to use the Hackathon ecosystem for offering a foresight on youth secondary employment and self-employment, the methods of abstract logical analysis and content analysis were used. Kyiv National University of Technologies and Design was chosen as the basis for this study. A questionnaire was chosen as a method of sociological research on student secondary employment. As a research toolkit, two questionnaires were developed: for students and for graduates. The study suggests using the Hackathon ecosystem to perform a foresight on student secondary employment and self-employment as a model to promote professional socialization of Ukrainian youth. According to the research findings, it is argued that there are three trajectories of student secondary employment: spontaneously formed (a good high paying job offered by chance), planned (targeted search for secondary employment according to the study major to gain professional competencies and work experience) and forced (employment to improve or maintain financial and economic well-being, usually beyond the education profile). An emphasis is put that secondary employment for Ukrainian students is not only the way to gain work experience and an extra pay opportunity but also an instrument of student professionalization. It is concluded that the terms and nature of secondary employment affect the professionalization effectiveness where gaining work experience, building professional contacts and employment prospects after graduation are viewed as a benefit for a wider student youth involvement in secondary employment.